2022 Projects - ESAC Trainees

2022 SCI-S TRAINEE PROJECTS

Below please see the list of the trainee projects being offered in 2022 by our Department.

For any questions we refer you to the contact page: https://www.cosmos.esa.int/web/esac-trainees/contact

ESAC

Improving collaboration efficiency on missions in study phase (process automation and optimization)
Finding Black Holes with a Neural Network
Comet Interceptor: Visualisation and geometry support for the flyby of a long-period comet or an interstellar object
Spectrophotometry of Enceladus’ surface: Comparisons with the Jupiter icy moons
Rosetta data tutorials with Jupyter Notebooks
Exploring the data from the X-ray cameras on-board XMM-Newton with Machine Learning
FLASHy alerts for interesting X-ray sources
CubeSat: Attitude Determination and Control System, and demonstrator
A new building block for the Gaia Catalogue of AGB Stars
Mercury science through databases and remote sensing observations: Increase the scientific exploitation of Mercury in view of BepiColombo
Characterization of Solar Wind Composition with Solar Orbiter Data
Enhancing SW license compliance process with Product Assurance tools
Developing interactive front- end for CESAR Space Science Experiences
Inclusion and Diversity in the CESAR Space Science Experiences
CESAR observatories operation support

ESTEC

Studying the brightest galaxies in the epoch of reionisation
Science of Gaia Science Research
Detecting Asteroids in the LISA datastream
Feeding and fuelling supermassive black-holes. The golden touch of X-ray spectroscopy with CHRESOS. Part I. - variability

at ESAC:

1. Improving collaboration efficiency on missions in study phase (process automation and optimization)

ESAC supervisor(s): Jakob Livschitz, Matthias Ehle

Two future missions in ESA’s space science programme will investigate some of the most extreme phenomena in the Universe: ATHENA, the Advanced Telescope for High-ENergy Astrophysics, and LISA, the Laser Interferometer Space Antenna. ATHENA will be the largest X-ray observatory ever built, investigating some of the hottest and most energetic phenomena in the cosmos with unprecedented accuracy and depth. LISA will be the first space-borne observatory of gravitational waves – fluctuations in the fabric of spacetime produced by the acceleration of cosmic objects with very strong gravity fields, like pairs of merging black holes.

Both missions are currently in the study phase, where efficient collaboration between geographically distributed teams is extremely crucial for the project success - especially during the COVID-19 pandemic, where physical interaction is limited. The projects rely on a set of collaborative and content management tools like JIRA, Confluence, DOORS, GitLab and so on. This internship is dedicated to following of current collaboration procedures, identifying of areas where higher degree of automation could be beneficial and implementation of this automation.

Project duration: 6 months.

Desirable expertise or programming language:

Software development (Python, Java, shell), knowledge of network protocols (HTTP, REST), common formats (JSON, XML).
Knowledge of DevOps approach, docker, of the toolchain currently used (JIRA, Confluence, GitLab, DOORS) as well as understanding of cyber-security aspects would be beneficial.

To apply for this project please fill in an online application form through the following link.

2. Finding Black Holes with a Neural Network

ESAC supervisor(s): Norbert Schartel, Maria Santos-Lleo (ESAC), Laura Manduchi (external collaborator at ETH University, Zürich, Switzerland), Guillermo Ayllon Perez (external collaborator at Complutense university of Madrid, Spain), Richard Saxton (ESAC), Felix Fuerst (ESAC)

XMM-Newton is an ESA space observatory that collects X-rays from astronomical sources. There are about 280,000 sources in the XMM-Newton Source Catalogue which have X-ray spectra. The majority of them were previously unknown and hence their intrinsic nature and physical origin are also unknown. The usual X-ray spectralanalysis technique involves a fitting process which requires making hypothesis about their physical nature and comparing the predictions with the observed spectra. This makes it very problematic to handle a large number of spectra.

In 2019 Laura Manduchi developed a neutral network architecture for the analysis of XMM-Newton spectra. Tests showed remarkable success and, especially, that the neutral network is superior to the fitting process. In 2021 Guillermo Ayllon started to test the neutral network procedures with real measured spectra. The developed codes together with the reports with detailed description of the results are available at ESAC for a continuation of the so far successful project.

The successful applicant will train the neural network and will process measured X-ray spectra from the XMM-Newton Catalogue with the trained neural network with the aim to identify supermassive black holes and characterise their physical parameters. The challenge is to use simulated spectra to train the neural network, because the number of measured spectra is insufficient to be used for training the neural network. The results will be documented on a technical report or in a paper to be submitted to the refereed literature, pending on the work outcome. The intention is to also make the code publicly available on a GitHub repository or a similar.

Outcome:

Basic experience in - Scientific research
Analysis of data from space observatories, like XMM-Newton,
X-ray astrophysics, including the production of simulated X-ray spectra from adopted physical models with different parameters
Artificial neural networks
Training, testing and analysing results of artificial neural networks
Potential to be published as an XMM-Newton technical note and/or paper in the scientific literature

Project duration: 3-6 months

Desirable expertise or programming language:

Physics, Astrophysics, Mathematics, Data Science or Software Engineer career paths are appropriate to opt to this project.
Previous experience with machine learning-based tools, like artificial neural networks would be very advantageous.

In addition one or more of the expertise listed below are desirable and will be considered in the selection, but they are not required:

Basic Astrophysical background, or Basic Data Science knowledge or Software Engineering background
Linux at user level, programming languages like Python or IDL,
XMM-Newton software (SAS) or with standard routines in X-ray astronomy like xspec or with astronomical databases (ADS, NED, Vizier …)

To apply for this project please fill in an online application form through the following link.

3. Comet Interceptor: Visualisation and geometry support for the flyby of a long-period comet or an interstellar object

ESAC supervisor(s): Michael Küppers, Charlotte Götz, Alfredo Escalante, Ricardo Valles

Comet Interceptor (https://www.cometinterceptor.space), to be launched in 2029, will be the first mission to encounter a long-period comet or an interstellar object. It consists of a main spacecraft and two small satellites that will be separated from the mother ship approximately one day before the comet flyby. The preparation of the flyby requires calculations of the geometry of the three spacecraft and the comet and a visualization tool. SPICE is used for the geometry and Cosmographia or Unity for the visualization (https://www.cosmos.esa.int/web/spice).

The purpose of the project is to improve the existing visualization of the Comet Interceptor flyby and to provide a tool that allows the user to implement updates to the flyby geometry (distance, comet properties, etc.) in a straightforward way.

Project duration: 6 months

Desirable expertise or programming language:

Basic knowledge of dynamics and/or planetary science
Knowledge of IDL, Python or C

To apply for this project please fill in an online application form through the following link.

4. Spectrophotometry of Enceladus’ surface: Comparisons with the Jupiter icy moons.

ESAC supervisor(s): Nicolas Altobelli, Ines Belgacem, Thomas Cornet

Enceladus is a natural icy moon of Saturn, which was regularly visited during flybys by the Cassini spacecraft between 2004 and 2017. With promising potential in the search for habitability in our solar system, due to the tidal heating of an internal water ocean protected by an icy crust, its surface is continuously renewed by the geological activity including cryovolcanism and its water vapor plumes erupting from Enceladus’ “tiger stripes” fractures near the South pole [1]. Studying the surface of Enceladus by analyzing Cassini data can also help us better understand the evolution of the other icy moons in the solar system, especially Europa, Ganymede and Callisto, which are primary targets of ESA’s future JUICE mission to the Jupiter system [2].

Using data acquired by the Cassini Visual and Infrared Mapping Spectrometer (VIMS) instrument with the varied geometric conditions of the Enceladus flybys, and powerful inversion methods, the successful applicant will study Enceladus’ regional photometry in different wavelengths and try to establish links between photometric parameters retrieved from the data modeling and the geological state of the surface. The results obtained for Enceladus will be compared to previous regional photometric studies of Europa [3] and Ganymede [4] in the growing context of the exploration of the Ocean Worlds of the solar system.

References:
[1] Spencer et al. Enceladus: An Active Cryovolcanic Satellite, In Saturn from Cassini-Huygens, Springer, 2009.
[2] Grasset et al., JUpiter ICy moons Explorer (JUICE): An ESA mission to orbit Ganymede and to characterise the Jupiter system, Planetary and Space Science, 78, 2013.
[3] Belgacem et al. Regional study of Europa’s photometry, Icarus, 338, 2020.
[4] Belgacem et al. Regional study of Ganymede’s photometry, Icarus, 369, 2021.

Project duration: 4-6 months

Desirable expertise or programming language:

Interest in planetary science and scientific research, icy moons’ science
Knowledge and/or interest in spectroscopy and/or photometry
Knowledge of matlab and/or python is mandatory as programming language
Knowledge of SQL relational database systems is an asset (e.g. MySQL, PostgreSQL)
Fluency in English is required

To apply for this project please fill in an online application form through the following link.

5. Rosetta data tutorials with Jupyter Notebooks

ESAC supervisor(s): Mark Bentley, David Heather

The successful Rosetta mission generated a wealth of data from a variety of instruments onboard the orbiter and lander – from cameras to spectrometers, microscopes to magnetometers. As such there is now a treasure trove of data ready for analysis, but the barrier to getting started with this is often high. This project proposes to write a series of Jupyter Notebooks to demonstrate how to search for and work with Rosetta data. An example of such a notebook is linked, written by one of the proposers for the Rosetta MIDAS instrument. The results of such notebooks could be made available, for example, on BitBucket and linked from ESA’s Rosetta Cosmos webpages to help future users starting to analyse the data. In addition, they would form the basis of “live” tutorials which could be embedded in the ESA DataLabs project. A recent enhancement from the community is a new python packaged called pdr (Planetary Data Reader) which would form the main tool used here to read the data – this open source library is designed to read all planetary archive data, and an additional benefit would be to fully validate this tool against the Rosetta data and raise tickets where necessary, contributing to the development of a key tool for working with all planetary data in the PSA.

Duration: 6 months

Desirable expertise or programming language:

Experience with python and Jupyter Notebooks is key.
Interest and background knowledge about the Rosetta mission and data analysis would be an advantage.

To apply for this project please fill in an online application form through the following link.

6. Exploring the data from the X-ray cameras on-board XMM-Newton with Machine Learning

ESAC supervisor(s): María Santos-Lleo, José Vicente Perea, Pedro Rodríguez

XMM-Newton observatory is the European Space Agency (ESA) flagship of X-ray astronomy. This space platform is able to perform simultaneous observations with all the science instruments it carries on-board. Among these instruments, there is a set of three X-ray CCD cameras called the European Photon Imaging Camera (EPIC). X-ray photons collected by the EPIC cameras are registered as individual events. The set of events collected during one observation is a multidimensional array, where every event is characterized by different properties, e.g. its position in the detector, energy and arrival time, among other. These data are processed by the XMM-Newton Science Operation Center (SOC) team following a strict set of algorithms and calibration methods to extract the scientific information.

The main focus of this internship experience is to explore new ways of processing the EPIC X-ray data by using machine learning techniques. Machine learning clustering methods have shown to be suitable to manage multidimensional data. In a recent pilot study, these methods were successfully applied to EPIC images to characterize the background noise, identify time intervals with low background noise and detect the astronomical sources in the images. The idea now is to move a big step further by applying similar algorithms to identify patterns and sources that are unnoticed with the standard processing methods. Detector characteristic features or, more important, astrophysical transient sources which are only shortly bright and not detected with standard methods will be unmasked in this way, helping to explore the partially unexplored timing information contained in more than 20 years of EPIC data.

The successful applicant will be able to interact with other interns working on similar projects at ESAC also offered in this call.

Project Duration: 6 months

Desirable expertise or programming language:

Python programming
Machine Learning clustering algorithms
XMM-Newton EPIC cameras data processing
Astronomical X-ray detection basics

To apply for this project please fill in an online application form through the following link.

7. FLASHy alerts for interesting X-ray sources

ESAC supervisor(s): Peter Kretschmar, Emilio Salazar, Celia Sanchez, Isabel Caballero

For the scientific community of high-energy astronomy missions like XMM-Newton or INTEGRAL, transient X-/gamma-ray sources or such sources undergoing major changes in flux or spectral shape are a major field of interest. Our project started with the creation of the BeXRB monitor, conceived as a service to the high-energy community. Created and run at ESAC, this tool collects and summarizes information from the existing all-sky monitors in X-rays. It is in regular use by astronomers around the world despite being limited to a subset of X-ray sources: the so-called BeX-ray binaries.

In recent times FLASHES (FLexible Alert Sytem for High Energy Sources) a similar, but enhanced tool with a much larger scope of covered sources has been developed at ESAC, as a research project involving a software engineer, X-ray astronomers and student efforts. The first version of this tool is expected to be made available before the end of 2021. But, as usual, there are many more ideas to enhance this tool and its usefulness for the community.

We are now looking for a student ready to become involved in this project, focussing on:
(1) Improvements to the FLASHES web interface, driven by community feedback
(2) Development of an automated system to alert registered users to sources of their interest, if they show interesting behaviour.

The work will be done in tight collaboration and under guidance of the existing team at ESAC. The student will acquire experience in web technologies, systematic software development and the variable sources of X-ray astronomy.

Project duration: 6 months

Desirable expertise or programming language:

Programming experience in Python
Some experience in developing with Web frontend technologies like Javascript, Bokeh, or similar
An astronomy background, especially X-ray astronomy would be an asset

To apply for this project please fill in an online application form through the following link.

8. CubeSat: Attitude Determination and Control System, and demonstrator

ESAC supervisor(s): Xavier Dupac, Julio Gallegos, Marcos López-Caniego

A CubeSat is a nano-satellite originally developed a as a project to support hands-on university-level space education and opportunities for low-cost space access; but since its introduction almost 20 years ago, they have gained a slot in the space industry and research, with multiple applications from Earth observation, variability of stars, or deep space exploration.

The prime objective of this project is to integrate the attitude determination and control system based on reaction wheels with magneto-torquers; this will include the definition of the validation of the system: design of experiments and the hardware needed (e.g., Helmholtz, air-bearing demonstrator). The two sensor and actuator sets (gyro+RW and MS+MT) will work together to demonstrate the CubeSat ADCS can achieve high accuracy in determining and controlling the attitude of the spacecraft for astronomical applications.

A ground station (GS) for CubeSats (UHF/VHF/S-band) is operational since mid-2017 at ESAC. The CubeSat communication system needs to be updated to include S-band for telemetry, so you can also work in this area and also operate the antenna with operational cubesats from entities around the world.

If you enjoy designing circuits, you will have a blast working in this project; as you will also be working on the design, implementation and testing of other subsystems (already quite advanced): power subsystem, communication and onboard computer (based on TI LaunchPad).

The main objectives are:

Integrate the ADCS based on magneto-sensors and magneto-torquers
Design the tests to validate the ADCS system
Update the GS to S-band
Test the remote accessibility to the GS operations
Complete the power, communication and onboard computer

Additionally, you will have the opportunity to design outreach activities for young students related to your work on the cubesat. You will have a great time learning and working on all aspects of the project.

Project duration: 6 months

Desirable expertise or programming language:

C/Python
Use of microprocessors (ARM, raspberry-π, etc.),
Control theory
Communication systems
Circuit design and implementation
A previous course on Spacecraft System Engineering will be certainly useful.

To apply for this project please fill in an online application form through the following link.

9. A new building block for the Gaia Catalogue of AGB Stars

ESAC supervisor(s): Pedro García Lario, Belén López Martí, Fran Jiménez Esteban

Asymptotic Giant Branch (AGB) stars are post-main sequence stars evolving towards the Planetary Nebula phase. They are characterized by envelopes of gas and dust, strong winds and in part maser emission. The dusty envelopes make them very bright in the infrared and often highly extincted in the visible range. Before the launch of ESA’s Gaia mission, only a few of these stars had reliable distance estimations. Our team is working on the construction of the Gaia Catalogue of AGB Stars made of well-characterised AGB stars with photometry, proper motions, distances, bolometric fluxes and luminosities, based on data from Gaia and other space and ground-based surveys covering the range from the optical to the far-infrared. It currently contains AGB star samples showing OH maser emission and infrared features indicating carbon-rich chemistry. This catalogue will become a benchmark for future studies of the population of AGB stars in the solar neighborhood. The selected student will cross-correlate oxygen-rich AGB star (candidates) from a recent compilation published by Suh (2021) with Gaia EDR3 and identify the stars with reliable distance estimations. The sample constitutes a new building block for our catalogue and its secondary products. The student will be introduced to Virtual Observatory tools and learn their application for astrophysical research.

Project duration: 3 to 6 months

Desirable expertise or programming language:

Good level of English (B2 or higher in the scale of the European Framework of Languages).
Basic Astronomy knowledge.
Interest in computer-based data analysis using VO tools is required.
Experience working with astronomical data would be helpful.

To apply for this project please fill in an online application form through the following link.

10. Mercury science through databases and remote sensing observations: Increase the scientific exploitation of Mercury in view of BepiColombo

ESAC supervisor(s): Santa Martinez, Sebastien Besse (ESAC), Claudio Munoz (ESAC), Thomas Cornet (ESAC), Alain Doressoundiram (University Paris Sciences Lettres)

The MESSENGER observations of Mercury have paved the road for ESA/JAXA BepiColombo mission to further explore the characteristics of Mercury's surface. A large diversity of scientific interests feed a very active scientific community that is analysing the available observations to better plan the upcoming one. Access to the data are granted via archives, but their services rarely allow for an efficient analysis of all the data and metadata provided by the science team.

The MeSS (Mercury Surface Spectroscopy) [1] project is providing an easy and improved access to calibrated and derived data for scientists. Successful investigations of volcanic phenomenon and volatile degassing processes [2,3,4,5] have revealed the great potential of using such relational databases for scientific analysis and more can certainly be done in the coming years.

This project will expand the content of the MeSS database hosted at ESAC. The PostgreSQL database contain observations from the MASCS visible and near infrared spectrometer of MESSENGER, and more should be added. The MeSS team has identified MESSENGER observations from the camera and altimeter instruments (MDIS and MLA) as key data sets to enhance the science analysis. Additionally, numerous derived products are shared within the community, but not accessible within a database framework. This concerns for instance topographic and composition maps but not only. The project will focus on the addition of those products into the MeSS database, while ensuring a reliable and optimised service to retrieve and analyse the data.

The work will be performed at ESAC/Spain, in collaboration with the MeSS team, the BepiColombo Science Ground Segment, and the BepiColombo Science Team.

References:
[1] https://www.cosmos.esa.int/web/space-science-faculty/research/mercury
[2] https://doi.org/10.1016/j.icarus.2020.114180
[3] https://doi.org/10.1016/j.icarus.2021.114652
[4] https://doi.org/10.1029/2020JE006497
[5] https://doi.org/10.1029/2018JE005879

Project duration: 3 to 6 months

Desirable expertise or programming language:

Interest in planetary science and scientific research is required
Knowledge on PostgreSQL database management and programming language(s) allowing connecting and handling PostgreSQL (e.g. python) will be needed.
Knowledge and interest in data mining, data visualisation and Geographical Information System (GIS) would be beneficial

To apply for this project please fill in an online application form through the following link.

11. Characterization of Solar Wind Composition with Solar Orbiter Data

ESAC supervisor(s): Andrew Walsh, Nils Janitzek, Yannis Zouganelis

Solar Orbiter (SOLO) is the most recent ESA/NASA science mission to study the Sun and the inner heliosphere. In the next year of operation the SOLO spacecraft will approach the Sun as close as 0.3 astronomical units for the first time and allow unprecedented measurements with a comprehensive suite of in-situ and remote-sensing instruments.

Eruptions on the Sun, that release large amounts of plasma from the solar atmosphere can lead to the formation of plasma shocks that travel through interplanetary space and efficiently accelerate particles to high energies. Yet, we are lacking a quantitative understanding which particles are injected at these shocks and accelerated most efficiently. Potential seed populations include both the solar wind, as a continuous stream of low-energetic solar particles, and suprathermal particles of different origin.
To quantify this seed population and allow a better understanding of solar particle acceleration we have at ESAC an ongoing study that focuses on the comparison of the ion composition between solar wind, suprathermal and energetic particles based on the new SOLO measurements.

The trainee will support this investigation by analyzing measurements of the Solar Wind Analyzer (SWA) Heavy Ion Sensor (HIS) onboard Solar Orbiter. In particular, the trainee will develop and apply statistical optimization methods to derive solar wind abundances for a wide range of elements. These can be then compared to the high-energetic particle composition of solar events. In addition, first evaluations of the derived ion compositions can be made by comparison of the SWA/HIS data with the first nominal Solar Orbiter remote-sensing measurements in spring 2022.

Project duration: 6 months

Desirable expertise or programming language:

First experience in the analysis of spaceborne particle measurements
Solid experience with a scientific programming language (preferably Python)

To apply for this project please fill in an online application form through the following link.

12. Enhancing SW license compliance process with Product Assurance tools

ESAC supervisor(s): Fernando Aldea Montero, Helena Vicente, Julio Gallegos, Isa Barbarisi

The ESAC PA function provides technical support to the local ESLB (ESAC Software License Board), with the use of certain tools that allow to verify software compliance with the applicable license conditions.

The objective of this activity is to expand the range of tools that can be used for this purpose, in order to cover all angles of the license compliance process.

A second objective is to integrate some of these tools in the software development chain in order to automate the license compliance process as much as possible, including the automatic generation of reports that can be used as evidence of license compliance.

Duration: 6 months

Desirable expertise or programming language:

Software engineering background.
Basic Knowledge of software quality assurance practices and techniques.
Basic knowledge of programming languages (e.g. Java, Python, HTML).
Knowledge of some software quality tools (e.g. SonarQube, findBugs, ScanCode).
Basic knowledge of software licenses and their implications.

To apply for this project please fill in an online application form through the following link.

13. Developing interactive front- end for CESAR Space Science Experiences

ESAC supervisor(s): Gaitee Hussain, Beatriz González García

The CESAR Team would like to update their producs to ensure they are accessible to children of all genders and diverse backgrounds to increase their engagement in Space and STEM and increase their familiarity with Space careers. This internship should aim at adapting CESAR Science Cases to student-oriented enviroments, making use of IT applications, VR and available e-learning platforms compatible with ESA Data Protection. The existing CESAR materials will be included in this new interactive environment, allowing students to perform the Space Science Experiences through it. This platform should also contain a back-end that permit evaluators to flexibly interact with the students individually.

Project Duration: 6 months

Desirable expertise or programming language:

Good understanding of astronomical concepts
Good background in software development for apps and e-learning platforms and telescope front-ends for students
With pedagological skills, specially when interacting with 4-18 year-old children
Experience designing education materials for inclusive and diverse environments
Capable of working in international teams and alone
Fluent in English (reading and writing)
Creative
Capable of meeting milestones within schedules.

To apply for this project please fill in an online application form through the following link.

14. Inclusion and Diversity in the CESAR Space Science Experiences

ESAC supervisor(s): Gaitee Hussain, Beatriz González García

The CESAR Team would like to update their producs to ensure they are accessible to children of all genders and diverse backgrounds to increase their engagement in Space and STEM and increase their familiarity with Space careers. This internship would involve reviewing CESAR Science Cases to suggest improvements and to prepare updated materials. The generated materials will be included in the ongoing Space Science Experiences (online and onsite, when possible) so the intern would also support these Space Science Experiences.

Project Duration: 6 months

Desirable expertise or programming language:

Good understanding of astronomical concepts
Good background in maths and physics
Pedagological skills, particularly related to (4-18) year-old children
Familiarity with inclusive and diverse environments,
Good skills in communicating scientific concepts to 4-18 year-old students
Fluent (reading & writing) in English
Creativity
Capable of working in international teams and alone
Capable of meeting milestones within schedules

To apply for this project please fill in an online application form through the following link.

15. CESAR observatories operation support

ESAC supervisor(s): Gaitee Hussain, Miguel Pérez-Ayúcar, David Gonzalez

The CESAR Team will resume the automation of onsite observatories operations. This applies to the two CESAR optical observatories at ESAC: Helios (solar telescopes) and Nyx (night telescopes). The observatories are composed of the dome/roof, mount, telescopes, cameras, focusers, guiding system, master computers, weather stations, webcams and network and power units.

The control of the Helios system is programmed via Unix, Python and ASCOM scripts and platforms. The control and operation of the night telescopes is still under definition. The traineeship will involve support to CESAR staff in:

support to definition and daily operations of the observatories (in particular Helios);
observatory hardware checking, mounting and testing;
evaluate the current control routines, defining improvements and debugging of the MASTER CONTROL program
support in the definition and implementation of the night telescopes monitoring and control
further improvements and debugging of the solar master control programs
implementation and test of high-def daily image of the Sun

Project Duration: 6 months

Desirable expertise or programming language:

Good knowledge of physics and mathematics, and understanding of astronomical concepts
Practical experience in Python programming and UNIX environments.
Experience with image processing.
Knowledge of programs for astronomical equipment control (dome, mount, cameras...) in particular ASCOM, Talon, Indi...
Knowledge in astronomical equipment for ground-based observatories
Practical experience in amateur observing would be an asset.
Capable of working in international teams and alone
Fluent in English (reading and writing)
Capable of meeting milestones within schedules.

To apply for this project please fill in an online application form through the following link.

at ESTEC:

16. Studying the brightest galaxies in the epoch of reionisation

ESTEC supervisor(s): Rachana Bhatawdekar, Pierre Ferruit, Giovanna Giardino

The epoch of reionisation when the Universe transformed from a neutral state to an ionised state is an important phase change in the history of our Universe. But what were the first luminous sources responsible for this phase change and when exactly did they form? These are some of the major unanswered questions in extragalactic astronomy.

The James Webb Space Telescope (JWST) will be launched in 2021 and will search for the First Light objects in the redshift range of z=10-15. Observing these galaxies will be routine with JWST, however, until then, our best chance to study some of them is to find the magnified ones through deep observations of lensing clusters with the Hubble Space Telescope (HST) by using them as `Cosmic Telescopes'. In this context, the Hubble Frontier Fields (HFF) program observed six massive clusters of galaxies with in particular the goal of using them as gravitational lenses to detect some of the faintest and earliest galaxies in the Universe, typically ~10-100 times fainter than any previously studied.

With the help of an innovative method (Bhatawdekar et al. 2019), we were able to detect a large number of faint galaxies (~40 times fainter than the current observational limits of Hubble) and identified among these the lowest mass galaxies ever observed at a distance corresponding to when the Universe was just 500 million years old. However, a complete understanding of the first galaxies requires an extended sample, including galaxies covering the full range of magnitudes required for reionization. This in turn requires observing the exceptionally rare brightest and most massive galaxies, in addition to the faintest, lowest mass galaxies.

To this end, this project will focus on finding and studying the brightest high redshift galaxies in the first billion years after the Big Bang using the HFF parallel fields imaging data. This would involve a student learning photometry techniques to construct multiwavelength photometry catalogs, as well as deriving galaxy properties such as photometric redshifts, luminosities, stellar masses and star-formation rates. The estimation of these properties for the brightest objects will allow us to sample the entire range of galaxy luminosities that are required for the observed level of reionisation and will provide us with a great opportunity to understand how galaxies evolve in the first billion years after the Big Bang. The study will also potentially provide targets for spectroscopic observations with JWST and it is therefore an excellent opportunity to get involved in the preparatory work for JWST. The results of the project could also potentially lay the foundation of a paper.

Project Duration: 3-6 months

Desirable expertise or programming language:

Basic astrophysics background and understanding of statistics
The existing codes are written in Python so some experience with coding languages (like Python, Matlab, IDL etc) is essential.
Some experience with Unix is beneficial.
Knowledge of image analysis software like SExtractor, IRAF would be an asset.

To apply for this project please fill in an online application form through the following link.

17. Science of Gaia Science Research

ESTEC supervisor(s): Jos de Bruijne, Héctor Canovas

Gaia is ESA’s current flagship science mission surveying the brightest ~2 billion objects in the sky and collecting astrometric, photometric, and spectroscopic measurements. The Gaia ESA Archive is the main access point to mine this goldmine of data. Underlying the thousands of scientific papers that have been published so far lie tens of thousands of queries of the Gaia ESA Archive. This trainee project aims to perform scientific meta-research by analysing Astronomical Data Query Language (ADQL) queries of the Gaia Archive to investigate how people use the Archive and access the Gaia data. The ultimate goal of this project is to develop strategic views for and optimise future data releases, new Archive features, and user support. The trainee would get access to a large set of (anonymised) queries and PostgreSQL statistics and would, for instance, investigate:

the popularity of certain types of queries and data fields (table columns) used in those queries,
the statistics on the various Archive access modes (simple web interface / ConeSearch, advanced web interface / ADQL queries, astroquery, etc.),
the presence of common syntax errors or failure conditions and causes,
the most time consuming queries / operations (e.g., inefficient JOINs because parameters of interest are split on different tables, use of non-indexed fields, etc.),
the usage of mathematical functions, data-type casting functions, conditional expressions, epoch propagation functions, etc.,
the usage of cross-match tables, external catalogues, tables at external data centres, etc.

Such studies will reveal a wealth of information, not only relevant for the future of the Gaia ESA Archive, but also for many other ESA science archives, such as XMM-Newton, Herschel, EUCLID, etc.

Project Duration: 6 months

Desirable expertise or programming language:

Most suitable for a student in astronomy with an interest in computer science/big data or a student in computer science/big data with an interest in astronomy.
Affinity with Python and ADQL is an asset.

To apply for this project please fill in an online application form through the following link.

18. Detecting Asteroids in the LISA datastream

ESTEC supervisor(s): Oliver Jennrich, Nora Lützgendorf

Asteroids that qualify as Near Earth Objects have some probability to come close enough to one of the LISA S/C to cause a gravitational effect that can be observed in the data stream. The work will be based on existing tools that simulate LISA data streams and that allow to inject addditional signals into the data streams as well as tools that allow to question asteroid databases, calculate the gravitational effect o fthe asteroid n the spacecraft and to estimate the accuracy with which the parameters can be estimated. Currently still missing is a way to actually detect those signals in the data stream. The purpose of the traineeship/internship is to investigate different data analysis techniques for their suitabl=ility and based on the outcome to develop an automated way to detect those events in the LISA data stream.

Project Duration: 6 months

Desirable expertise or programming language:

The project will offer ample opportunity to improve your skills in the areas of Python programming, basic physics, and data analysis techniques.
Some familiarity in those areas wouyld be helpful, but is not really required.
Bring your enthusiasm for physics, data analysis and a spectacular future science mission of ESA.
Due to the complexity of the project, candidates that are interested in a 6 months internship/traineeship are preferred.

To apply for this project please fill in an online application form through the following link.

19. Feeding and fuelling supermassive black-holes. The golden touch of X-ray spectroscopy with CHRESOS. Part I. - variability

ESTEC supervisor(s): Matteo Guainazzi

Most, if not all galaxies in the Universe host a super-massive black hole (million to billion solar masses) in their nucleus. About 10% of them are “active”, i.e. they are fuelled by matter trapped by their immense gravitational potential. The gravitational energy of the infalling matter is efficiently converted into a radiation power outshining the integrated light of the stars in the whole galaxy.

Which physical processes lead to gas and dust in the interstellar medium to lose angular momentum and fall onto the black hole, and whether black holes affect the cosmological evolution of galaxies are amongst the most important questions in modern extragalactic astrophysics. One of the main issues preventing us from addressing them is the fact that most “active galaxies” are spatially unresolved on those scales where these processes are triggered.

We propose to address these questions by studying the largest existing sample of X-ray spectra of an important class of active galaxies (so-called “Seyfert 2s”) observed with the Reflection Grating Spectrometer on board the ESA’s X-ray observatory XMM-Newton. By studying the variability pattern of emission lines taken at different epochs in the framework of CHRESOS (“Catalogue of High-Resolution Spectra of Obscured Sources”) and using light-crossing arguments, the student will estimate the location of the gas illuminated by the radiation emitted by the black holes.

Project Duration: 6 months

Desirable expertise or programming language:

No formal pre-requisites for this project exist.
Knowledge of matter-radiation process (e.g., photoelectric absorption, photoionization collisional and radiative de-excitation, radiative recombination) would be an asset.
The project will require creating automated meta-analysis scripts to reduce and analyse a large number of observational spectra (~a few hundreds).
Knowledge of a programming language (Python, IDL, etc.) would be therefore an asset.

To apply for this project please fill in an online application form through the following link.